Overview

Dataset statistics

Number of variables12
Number of observations503
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.1 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
pH is highly overall correlated with fixed acidityHigh correlation
alcohol is highly overall correlated with qualityHigh correlation
quality is highly overall correlated with alcoholHigh correlation
citric acid has 38 (7.6%) zerosZeros

Reproduction

Analysis started2023-03-22 12:36:10.037485
Analysis finished2023-03-22 12:36:31.869400
Duration21.83 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct73
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1091451
Minimum5
Maximum13.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:32.027087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.1
Q17.1
median7.8
Q38.9
95-th percentile11.2
Maximum13.7
Range8.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.5571823
Coefficient of variation (CV)0.19202792
Kurtosis0.97741622
Mean8.1091451
Median Absolute Deviation (MAD)0.9
Skewness0.98928786
Sum4078.9
Variance2.4248166
MonotonicityNot monotonic
2023-03-22T18:06:32.216156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1 25
 
5.0%
7.8 22
 
4.4%
7.6 20
 
4.0%
7.2 20
 
4.0%
8 18
 
3.6%
7.7 18
 
3.6%
7.3 17
 
3.4%
7.5 17
 
3.4%
7 16
 
3.2%
6.9 15
 
3.0%
Other values (63) 315
62.6%
ValueCountFrequency (%)
5 2
 
0.4%
5.1 1
 
0.2%
5.2 2
 
0.4%
5.4 3
0.6%
5.6 4
0.8%
5.8 2
 
0.4%
5.9 4
0.8%
6 4
0.8%
6.1 6
1.2%
6.2 5
1.0%
ValueCountFrequency (%)
13.7 1
 
0.2%
13.5 1
 
0.2%
13.3 2
0.4%
12.6 1
 
0.2%
12.5 3
0.6%
12.4 3
0.6%
12.3 1
 
0.2%
12.2 1
 
0.2%
12 2
0.4%
11.9 1
 
0.2%

volatile acidity
Real number (ℝ)

Distinct103
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52593439
Minimum0.16
Maximum1.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:32.420680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.28
Q10.4
median0.52
Q30.63
95-th percentile0.84
Maximum1.04
Range0.88
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.17023543
Coefficient of variation (CV)0.32368187
Kurtosis0.077010508
Mean0.52593439
Median Absolute Deviation (MAD)0.12
Skewness0.47715125
Sum264.545
Variance0.028980101
MonotonicityNot monotonic
2023-03-22T18:06:32.619999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59 17
 
3.4%
0.5 14
 
2.8%
0.42 14
 
2.8%
0.43 13
 
2.6%
0.4 13
 
2.6%
0.58 13
 
2.6%
0.41 13
 
2.6%
0.36 12
 
2.4%
0.6 12
 
2.4%
0.48 12
 
2.4%
Other values (93) 370
73.6%
ValueCountFrequency (%)
0.16 2
 
0.4%
0.18 2
 
0.4%
0.19 1
 
0.2%
0.2 1
 
0.2%
0.21 1
 
0.2%
0.22 2
 
0.4%
0.23 2
 
0.4%
0.24 1
 
0.2%
0.25 4
0.8%
0.26 5
1.0%
ValueCountFrequency (%)
1.04 2
0.4%
1.025 1
 
0.2%
1.02 3
0.6%
0.98 2
0.4%
0.965 1
 
0.2%
0.935 1
 
0.2%
0.915 1
 
0.2%
0.91 2
0.4%
0.885 1
 
0.2%
0.88 4
0.8%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25737575
Minimum0
Maximum0.79
Zeros38
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:32.793540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.095
median0.26
Q30.39
95-th percentile0.55
Maximum0.79
Range0.79
Interquartile range (IQR)0.295

Descriptive statistics

Standard deviation0.17791699
Coefficient of variation (CV)0.69127332
Kurtosis-0.75676905
Mean0.25737575
Median Absolute Deviation (MAD)0.15
Skewness0.25715593
Sum129.46
Variance0.031654454
MonotonicityNot monotonic
2023-03-22T18:06:32.984391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
7.6%
0.49 25
 
5.0%
0.02 18
 
3.6%
0.24 17
 
3.4%
0.26 17
 
3.4%
0.32 14
 
2.8%
0.08 14
 
2.8%
0.23 13
 
2.6%
0.21 12
 
2.4%
0.03 12
 
2.4%
Other values (58) 323
64.2%
ValueCountFrequency (%)
0 38
7.6%
0.01 9
 
1.8%
0.02 18
3.6%
0.03 12
 
2.4%
0.04 8
 
1.6%
0.05 5
 
1.0%
0.06 7
 
1.4%
0.07 7
 
1.4%
0.08 14
 
2.8%
0.09 8
 
1.6%
ValueCountFrequency (%)
0.79 1
 
0.2%
0.75 1
 
0.2%
0.73 1
 
0.2%
0.7 1
 
0.2%
0.69 1
 
0.2%
0.68 1
 
0.2%
0.64 1
 
0.2%
0.63 1
 
0.2%
0.6 3
0.6%
0.59 1
 
0.2%

residual sugar
Real number (ℝ)

Distinct44
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3915507
Minimum1.2
Maximum6.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:33.203721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile3.89
Maximum6.55
Range5.35
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.77379464
Coefficient of variation (CV)0.32355352
Kurtosis7.6025743
Mean2.3915507
Median Absolute Deviation (MAD)0.3
Skewness2.3375473
Sum1202.95
Variance0.59875815
MonotonicityNot monotonic
2023-03-22T18:06:33.396539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2.5 45
 
8.9%
2 45
 
8.9%
1.8 39
 
7.8%
1.9 37
 
7.4%
2.1 36
 
7.2%
2.2 34
 
6.8%
2.3 33
 
6.6%
2.6 28
 
5.6%
2.4 27
 
5.4%
2.8 23
 
4.6%
Other values (34) 156
31.0%
ValueCountFrequency (%)
1.2 1
 
0.2%
1.3 1
 
0.2%
1.4 7
 
1.4%
1.5 11
 
2.2%
1.6 22
4.4%
1.7 20
4.0%
1.75 1
 
0.2%
1.8 39
7.8%
1.9 37
7.4%
2 45
8.9%
ValueCountFrequency (%)
6.55 1
 
0.2%
6.2 1
 
0.2%
6.1 2
0.4%
5.8 1
 
0.2%
5.6 3
0.6%
5.5 2
0.4%
5.2 2
0.4%
4.8 1
 
0.2%
4.6 1
 
0.2%
4.5 1
 
0.2%

chlorides
Real number (ℝ)

Distinct85
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.080872763
Minimum0.038
Maximum0.171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:33.828803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.058
Q10.072
median0.08
Q30.088
95-th percentile0.1089
Maximum0.171
Range0.133
Interquartile range (IQR)0.016

Descriptive statistics

Standard deviation0.016603884
Coefficient of variation (CV)0.20530872
Kurtosis4.2992343
Mean0.080872763
Median Absolute Deviation (MAD)0.008
Skewness1.1358773
Sum40.679
Variance0.00027568896
MonotonicityNot monotonic
2023-03-22T18:06:34.027097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 26
 
5.2%
0.078 24
 
4.8%
0.076 19
 
3.8%
0.079 19
 
3.8%
0.077 17
 
3.4%
0.074 17
 
3.4%
0.081 17
 
3.4%
0.082 15
 
3.0%
0.083 15
 
3.0%
0.071 14
 
2.8%
Other values (75) 320
63.6%
ValueCountFrequency (%)
0.038 1
 
0.2%
0.041 1
 
0.2%
0.042 1
 
0.2%
0.044 2
0.4%
0.045 2
0.4%
0.046 1
 
0.2%
0.047 1
 
0.2%
0.048 1
 
0.2%
0.049 3
0.6%
0.05 2
0.4%
ValueCountFrequency (%)
0.171 1
0.2%
0.169 1
0.2%
0.147 1
0.2%
0.146 1
0.2%
0.145 1
0.2%
0.136 1
0.2%
0.132 1
0.2%
0.126 1
0.2%
0.124 1
0.2%
0.122 2
0.4%

free sulfur dioxide
Real number (ℝ)

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.559642
Minimum16
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:34.213036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16
Q119
median23
Q329
95-th percentile38
Maximum45
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.9817221
Coefficient of variation (CV)0.28427622
Kurtosis-0.22845046
Mean24.559642
Median Absolute Deviation (MAD)5
Skewness0.74072081
Sum12353.5
Variance48.744444
MonotonicityNot monotonic
2023-03-22T18:06:34.369026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
17 48
 
9.5%
16 43
 
8.5%
21 36
 
7.2%
18 34
 
6.8%
19 30
 
6.0%
26 27
 
5.4%
24 26
 
5.2%
23 25
 
5.0%
20 24
 
4.8%
27 21
 
4.2%
Other values (20) 189
37.6%
ValueCountFrequency (%)
16 43
8.5%
17 48
9.5%
18 34
6.8%
19 30
6.0%
20 24
4.8%
21 36
7.2%
22 16
 
3.2%
23 25
5.0%
24 26
5.2%
25 20
4.0%
ValueCountFrequency (%)
45 3
0.6%
43 3
0.6%
42 3
0.6%
41 5
1.0%
40.5 1
 
0.2%
40 4
0.8%
39 4
0.8%
38 7
1.4%
37 3
0.6%
36 5
1.0%

total sulfur dioxide
Real number (ℝ)

Distinct120
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.314115
Minimum20
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:34.553030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile29
Q144
median58
Q385
95-th percentile128.9
Maximum165
Range145
Interquartile range (IQR)41

Descriptive statistics

Standard deviation30.157963
Coefficient of variation (CV)0.45477441
Kurtosis0.36962128
Mean66.314115
Median Absolute Deviation (MAD)16
Skewness0.97876036
Sum33356
Variance909.50273
MonotonicityNot monotonic
2023-03-22T18:06:34.739029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 13
 
2.6%
48 12
 
2.4%
49 12
 
2.4%
52 12
 
2.4%
47 12
 
2.4%
53 11
 
2.2%
38 11
 
2.2%
60 11
 
2.2%
44 11
 
2.2%
58 10
 
2.0%
Other values (110) 388
77.1%
ValueCountFrequency (%)
20 1
 
0.2%
22 1
 
0.2%
23 1
 
0.2%
24 3
0.6%
25 3
0.6%
26 4
0.8%
27 5
1.0%
28 4
0.8%
29 5
1.0%
30 3
0.6%
ValueCountFrequency (%)
165 1
 
0.2%
155 1
 
0.2%
153 1
 
0.2%
152 1
 
0.2%
151 1
 
0.2%
149 1
 
0.2%
147 2
0.4%
145 2
0.4%
144 3
0.6%
143 1
 
0.2%

density
Real number (ℝ)

Distinct261
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99671851
Minimum0.9912
Maximum1.0018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:34.911880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9912
5-th percentile0.993805
Q10.9956
median0.99672
Q30.99783
95-th percentile0.99969
Maximum1.0018
Range0.0106
Interquartile range (IQR)0.00223

Descriptive statistics

Standard deviation0.0017005476
Coefficient of variation (CV)0.0017061463
Kurtosis0.20433749
Mean0.99671851
Median Absolute Deviation (MAD)0.00112
Skewness0.048954729
Sum501.34941
Variance2.8918621 × 10-6
MonotonicityNot monotonic
2023-03-22T18:06:35.125485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9982 12
 
2.4%
0.9968 11
 
2.2%
0.998 11
 
2.2%
0.9976 10
 
2.0%
0.9972 9
 
1.8%
0.9978 8
 
1.6%
0.9984 7
 
1.4%
0.9956 7
 
1.4%
0.9962 7
 
1.4%
0.997 6
 
1.2%
Other values (251) 415
82.5%
ValueCountFrequency (%)
0.9912 1
0.2%
0.9922 2
0.4%
0.99236 1
0.2%
0.9924 1
0.2%
0.99258 1
0.2%
0.9927 1
0.2%
0.99294 1
0.2%
0.99314 1
0.2%
0.99328 1
0.2%
0.9934 1
0.2%
ValueCountFrequency (%)
1.0018 1
 
0.2%
1.0014 2
0.4%
1.001 3
0.6%
1.0008 1
 
0.2%
1.0004 2
0.4%
1.0003 1
 
0.2%
1.00025 1
 
0.2%
1.00024 1
 
0.2%
1.0002 3
0.6%
1.00015 1
 
0.2%

pH
Real number (ℝ)

Distinct68
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.326501
Minimum2.92
Maximum3.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:35.335489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.92
5-th percentile3.1
Q13.23
median3.32
Q33.41
95-th percentile3.569
Maximum3.78
Range0.86
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.13883007
Coefficient of variation (CV)0.041734564
Kurtosis0.0084751342
Mean3.326501
Median Absolute Deviation (MAD)0.09
Skewness0.16399179
Sum1673.23
Variance0.019273788
MonotonicityNot monotonic
2023-03-22T18:06:35.532731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.32 19
 
3.8%
3.34 19
 
3.8%
3.3 18
 
3.6%
3.38 18
 
3.6%
3.31 18
 
3.6%
3.28 17
 
3.4%
3.39 17
 
3.4%
3.26 17
 
3.4%
3.4 14
 
2.8%
3.15 14
 
2.8%
Other values (58) 332
66.0%
ValueCountFrequency (%)
2.92 1
 
0.2%
2.98 1
 
0.2%
3.02 3
 
0.6%
3.04 2
 
0.4%
3.05 2
 
0.4%
3.06 3
 
0.6%
3.08 5
1.0%
3.09 1
 
0.2%
3.1 12
2.4%
3.11 1
 
0.2%
ValueCountFrequency (%)
3.78 1
 
0.2%
3.75 1
 
0.2%
3.74 1
 
0.2%
3.71 1
 
0.2%
3.68 1
 
0.2%
3.67 1
 
0.2%
3.63 1
 
0.2%
3.62 1
 
0.2%
3.61 2
 
0.4%
3.6 5
1.0%

sulphates
Real number (ℝ)

Distinct67
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64508946
Minimum0.4
Maximum1.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:35.738917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.48
Q10.55
median0.62
Q30.72
95-th percentile0.869
Maximum1.14
Range0.74
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.126975
Coefficient of variation (CV)0.19683317
Kurtosis0.96656604
Mean0.64508946
Median Absolute Deviation (MAD)0.08
Skewness0.92764609
Sum324.48
Variance0.016122651
MonotonicityNot monotonic
2023-03-22T18:06:35.924456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53 24
 
4.8%
0.57 22
 
4.4%
0.58 22
 
4.4%
0.6 21
 
4.2%
0.54 21
 
4.2%
0.62 20
 
4.0%
0.61 18
 
3.6%
0.59 17
 
3.4%
0.52 16
 
3.2%
0.64 15
 
3.0%
Other values (57) 307
61.0%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.42 2
 
0.4%
0.43 4
 
0.8%
0.44 1
 
0.2%
0.45 4
 
0.8%
0.46 2
 
0.4%
0.47 3
 
0.6%
0.48 14
2.8%
0.49 7
1.4%
0.5 8
1.6%
ValueCountFrequency (%)
1.14 1
0.2%
1.12 1
0.2%
1.11 1
0.2%
1.07 1
0.2%
1.04 1
0.2%
1.03 1
0.2%
1.02 1
0.2%
1.01 1
0.2%
1 1
0.2%
0.98 1
0.2%

alcohol
Real number (ℝ)

Distinct49
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.288304
Minimum8.5
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:36.112473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile9.2
Q19.5
median10
Q310.9
95-th percentile12.2
Maximum13
Range4.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.93679969
Coefficient of variation (CV)0.091054827
Kurtosis-0.077266921
Mean10.288304
Median Absolute Deviation (MAD)0.6
Skewness0.82141224
Sum5175.0167
Variance0.87759366
MonotonicityNot monotonic
2023-03-22T18:06:36.297416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9.5 44
 
8.7%
9.4 40
 
8.0%
9.8 25
 
5.0%
9.7 25
 
5.0%
9.2 25
 
5.0%
9.3 22
 
4.4%
10 22
 
4.4%
9.9 20
 
4.0%
10.1 19
 
3.8%
10.5 19
 
3.8%
Other values (39) 242
48.1%
ValueCountFrequency (%)
8.5 1
 
0.2%
8.7 1
 
0.2%
9 4
 
0.8%
9.1 7
 
1.4%
9.2 25
5.0%
9.233333333 1
 
0.2%
9.25 1
 
0.2%
9.3 22
4.4%
9.4 40
8.0%
9.5 44
8.7%
ValueCountFrequency (%)
13 2
 
0.4%
12.9 3
0.6%
12.8 2
 
0.4%
12.7 2
 
0.4%
12.6 2
 
0.4%
12.5 3
0.6%
12.4 5
1.0%
12.3 5
1.0%
12.2 3
0.6%
12.1 4
0.8%

quality
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5606362
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-22T18:06:36.457942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median5
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72229795
Coefficient of variation (CV)0.12989484
Kurtosis0.49954414
Mean5.5606362
Median Absolute Deviation (MAD)1
Skewness0.34429231
Sum2797
Variance0.52171434
MonotonicityNot monotonic
2023-03-22T18:06:36.593952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 239
47.5%
6 205
40.8%
7 42
 
8.3%
4 12
 
2.4%
8 3
 
0.6%
3 2
 
0.4%
ValueCountFrequency (%)
3 2
 
0.4%
4 12
 
2.4%
5 239
47.5%
6 205
40.8%
7 42
 
8.3%
8 3
 
0.6%
ValueCountFrequency (%)
8 3
 
0.6%
7 42
 
8.3%
6 205
40.8%
5 239
47.5%
4 12
 
2.4%
3 2
 
0.4%

Interactions

2023-03-22T18:06:29.408980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.188010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.886887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.616704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.362782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.862401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.852589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.821470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.027867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:22.310675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:24.687508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:27.316132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:29.586353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.250010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.938888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.696217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.416838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.026192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.994066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:18.145860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.215456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:22.525315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:24.867755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:27.478373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:29.748849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.302006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.991884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.759518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.471513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.192635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:16.164898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:18.313870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.400975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:22.738782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:25.072902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:27.634256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:29.918997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.360006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.112890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.822996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.534515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.371501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:16.349662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:18.499861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.591308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:22.935015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:25.265165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:27.823203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.086442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.413323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.168468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.883583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.588511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.540154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:16.525115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:18.666615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.793963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:23.151525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:25.716221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.059354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.255311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.469322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.225467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.942581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.754276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.712451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:16.699394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:18.842579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:20.981369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:23.360766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:25.923093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.230278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.426077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.535484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.284470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.006770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.913115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:14.847210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:16.868495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.009452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:21.164091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:23.556541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:26.140834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.392178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.609608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.613026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.342467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.070649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.086338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.028177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.010195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.179978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:21.365812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:23.793330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:26.322730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.557997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.748110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.671391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.398469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.130646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.250994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.192111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.176502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.356297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:21.534758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:23.971879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:26.506380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.713806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:30.904835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.724374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.450467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.185645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.403664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.353172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.334478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.516719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:21.700104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:24.162838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:26.716934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:28.879606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:31.079054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.781887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.509699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.247915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.583482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.533369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.505465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.693684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:21.952614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:24.330377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:26.898524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:29.049222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:31.230332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:10.832888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:11.561700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:12.303334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:13.738867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:15.697256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:17.661471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:19.859869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:22.123123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:24.529883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:27.103502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T18:06:29.212584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-22T18:06:36.753547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.2010.6030.2750.3380.0660.1700.650-0.6570.163-0.130-0.000
volatile acidity-0.2011.000-0.5980.0840.132-0.0500.0950.0340.152-0.330-0.184-0.297
citric acid0.603-0.5981.0000.1460.1180.0830.2280.343-0.4690.3080.0080.117
residual sugar0.2750.0840.1461.0000.301-0.0110.2380.494-0.1680.031-0.048-0.065
chlorides0.3380.1320.1180.3011.000-0.0420.2350.473-0.279-0.101-0.316-0.218
free sulfur dioxide0.066-0.0500.083-0.011-0.0421.0000.335-0.035-0.0780.0490.028-0.065
total sulfur dioxide0.1700.0950.2280.2380.2350.3351.0000.297-0.235-0.179-0.380-0.336
density0.6500.0340.3430.4940.473-0.0350.2971.000-0.2720.104-0.465-0.237
pH-0.6570.152-0.469-0.168-0.279-0.078-0.235-0.2721.0000.0460.2140.051
sulphates0.163-0.3300.3080.031-0.1010.049-0.1790.1040.0461.0000.3810.396
alcohol-0.130-0.1840.008-0.048-0.3160.028-0.380-0.4650.2140.3811.0000.554
quality-0.000-0.2970.117-0.065-0.218-0.065-0.336-0.2370.0510.3960.5541.000

Missing values

2023-03-22T18:06:31.465319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-22T18:06:31.733281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
17.80.8800.002.60.09825.067.00.99683.200.689.85
311.20.2800.561.90.07517.060.00.99803.160.589.86
97.50.5000.366.10.07117.0102.00.99783.350.8010.55
125.60.6150.001.60.08916.059.00.99433.580.529.95
168.50.2800.561.80.09235.0103.00.99693.300.7510.57
208.90.2200.481.80.07729.060.00.99683.390.539.46
217.60.3900.312.30.08223.071.00.99823.520.659.75
246.90.4000.142.40.08521.040.00.99683.430.639.76
306.70.6750.072.40.08917.082.00.99583.350.5410.15
316.90.6850.002.50.10522.037.00.99663.460.5710.66
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
15875.80.6100.111.80.06618.028.00.994833.550.6610.96
15887.20.6600.332.50.06834.0102.00.994143.270.7812.86
15906.30.5500.151.80.07726.035.00.993143.320.8211.66
15915.40.7400.091.70.08916.026.00.994023.670.5611.66
15926.30.5100.132.30.07629.040.00.995743.420.7511.06
15936.80.6200.081.90.06828.038.00.996513.420.829.56
15946.20.6000.082.00.09032.044.00.994903.450.5810.55
15955.90.5500.102.20.06239.051.00.995123.520.7611.26
15975.90.6450.122.00.07532.044.00.995473.570.7110.25
15986.00.3100.473.60.06718.042.00.995493.390.6611.06